Overview

Dataset statistics

Number of variables15
Number of observations6156
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory721.5 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
employee_payroll is highly correlated with bad_dept_expence and 7 other fieldsHigh correlation
charity_care is highly correlated with bad_dept_expence and 1 other fieldsHigh correlation
bad_dept_expence is highly correlated with employee_payroll and 4 other fieldsHigh correlation
unreimbursed_and_uncompensated is highly correlated with employee_payroll and 6 other fieldsHigh correlation
Total Costs is highly correlated with employee_payroll and 6 other fieldsHigh correlation
outpatient_inpatient_charges is highly correlated with employee_payroll and 7 other fieldsHigh correlation
Contract Labor is highly correlated with discharge_paymentsHigh correlation
account_receivable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
assets is highly correlated with employee_payroll and 6 other fieldsHigh correlation
account_payable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
discharge_payments is highly correlated with Contract LaborHigh correlation
gross_revenue is highly correlated with employee_payroll and 7 other fieldsHigh correlation
employee_payroll is highly correlated with bad_dept_expence and 8 other fieldsHigh correlation
charity_care is highly correlated with bad_dept_expence and 1 other fieldsHigh correlation
bad_dept_expence is highly correlated with employee_payroll and 5 other fieldsHigh correlation
unreimbursed_and_uncompensated is highly correlated with employee_payroll and 6 other fieldsHigh correlation
Total Costs is highly correlated with employee_payroll and 8 other fieldsHigh correlation
outpatient_inpatient_charges is highly correlated with employee_payroll and 8 other fieldsHigh correlation
total_salaries is highly correlated with employee_payroll and 6 other fieldsHigh correlation
account_receivable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
assets is highly correlated with employee_payroll and 5 other fieldsHigh correlation
account_payable is highly correlated with assetsHigh correlation
discharge_payments is highly correlated with employee_payroll and 4 other fieldsHigh correlation
gross_revenue is highly correlated with employee_payroll and 8 other fieldsHigh correlation
employee_payroll is highly correlated with Total Costs and 5 other fieldsHigh correlation
charity_care is highly correlated with bad_dept_expence and 1 other fieldsHigh correlation
bad_dept_expence is highly correlated with charity_care and 1 other fieldsHigh correlation
unreimbursed_and_uncompensated is highly correlated with charity_care and 1 other fieldsHigh correlation
Total Costs is highly correlated with employee_payroll and 5 other fieldsHigh correlation
outpatient_inpatient_charges is highly correlated with employee_payroll and 5 other fieldsHigh correlation
account_receivable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
assets is highly correlated with employee_payroll and 5 other fieldsHigh correlation
account_payable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
gross_revenue is highly correlated with employee_payroll and 5 other fieldsHigh correlation
employee_payroll is highly correlated with bad_dept_expence and 9 other fieldsHigh correlation
charity_care is highly correlated with bad_dept_expence and 2 other fieldsHigh correlation
bad_dept_expence is highly correlated with employee_payroll and 2 other fieldsHigh correlation
unreimbursed_and_uncompensated is highly correlated with employee_payroll and 10 other fieldsHigh correlation
Total Costs is highly correlated with employee_payroll and 10 other fieldsHigh correlation
outpatient_inpatient_charges is highly correlated with employee_payroll and 9 other fieldsHigh correlation
total_salaries is highly correlated with employee_payroll and 8 other fieldsHigh correlation
Contract Labor is highly correlated with net_incomeHigh correlation
cash is highly correlated with charity_care and 5 other fieldsHigh correlation
account_receivable is highly correlated with employee_payroll and 8 other fieldsHigh correlation
assets is highly correlated with employee_payroll and 10 other fieldsHigh correlation
account_payable is highly correlated with employee_payroll and 5 other fieldsHigh correlation
discharge_payments is highly correlated with employee_payroll and 8 other fieldsHigh correlation
gross_revenue is highly correlated with employee_payroll and 9 other fieldsHigh correlation
net_income is highly correlated with unreimbursed_and_uncompensated and 6 other fieldsHigh correlation
charity_care is highly skewed (γ1 = 22.06018491) Skewed

Reproduction

Analysis started2022-02-09 20:31:45.368752
Analysis finished2022-02-09 20:32:18.024090
Duration32.66 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

employee_payroll
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5753
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean846.0510883
Minimum0.05
Maximum26491.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:18.255409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile50.85
Q1131.625
median315.8
Q3883.575
95-th percentile3226.49
Maximum26491.16
Range26491.11
Interquartile range (IQR)751.95

Descriptive statistics

Standard deviation1553.908104
Coefficient of variation (CV)1.836659896
Kurtosis53.49357737
Mean846.0510883
Median Absolute Deviation (MAD)233.62
Skewness5.757822692
Sum5208290.5
Variance2414630.395
MonotonicityNot monotonic
2022-02-10T02:32:18.566352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846.0510883110
 
1.8%
444
 
0.1%
584
 
0.1%
424
 
0.1%
614
 
0.1%
674
 
0.1%
364
 
0.1%
1063
 
< 0.1%
184.933
 
< 0.1%
90.323
 
< 0.1%
Other values (5743)6013
97.7%
ValueCountFrequency (%)
0.051
< 0.1%
0.271
< 0.1%
12
< 0.1%
1.711
< 0.1%
22
< 0.1%
2.11
< 0.1%
3.61
< 0.1%
4.741
< 0.1%
7.831
< 0.1%
8.861
< 0.1%
ValueCountFrequency (%)
26491.161
< 0.1%
243491
< 0.1%
217861
< 0.1%
21355.51
< 0.1%
18142.51
< 0.1%
18112.421
< 0.1%
181001
< 0.1%
17530.441
< 0.1%
16201.31
< 0.1%
145031
< 0.1%

charity_care
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4328
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4742004.031
Minimum6
Maximum595402233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:18.904864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17456.5
Q1378153.75
median2911040.5
Q34742004.031
95-th percentile15624186.75
Maximum595402233
Range595402227
Interquartile range (IQR)4363850.281

Descriptive statistics

Standard deviation13844007.85
Coefficient of variation (CV)2.919442445
Kurtosis753.4402479
Mean4742004.031
Median Absolute Deviation (MAD)1966281.5
Skewness22.06018491
Sum2.919177682 × 1010
Variance1.916565532 × 1014
MonotonicityNot monotonic
2022-02-10T02:32:19.185947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4742004.0311824
29.6%
810872
 
< 0.1%
374992
 
< 0.1%
1332162
 
< 0.1%
770632
 
< 0.1%
116802
 
< 0.1%
210781
 
< 0.1%
139761
 
< 0.1%
24358521
 
< 0.1%
1583851
 
< 0.1%
Other values (4318)4318
70.1%
ValueCountFrequency (%)
61
< 0.1%
611
< 0.1%
661
< 0.1%
1111
< 0.1%
1461
< 0.1%
1551
< 0.1%
1871
< 0.1%
1951
< 0.1%
2021
< 0.1%
2131
< 0.1%
ValueCountFrequency (%)
5954022331
< 0.1%
4348679791
< 0.1%
3092618651
< 0.1%
2042514141
< 0.1%
1952617271
< 0.1%
1881955011
< 0.1%
1489554481
< 0.1%
1429005411
< 0.1%
1309685061
< 0.1%
1180801081
< 0.1%

bad_dept_expence
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4665
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12278282.72
Minimum-185599
Maximum423740576
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size48.2 KiB
2022-02-10T02:32:19.624303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-185599
5-th percentile274692.5
Q12066813.75
median8944558.5
Q312278282.72
95-th percentile41207966.5
Maximum423740576
Range423926175
Interquartile range (IQR)10211468.97

Descriptive statistics

Standard deviation20281794.62
Coefficient of variation (CV)1.65184294
Kurtosis75.55241492
Mean12278282.72
Median Absolute Deviation (MAD)5659357
Skewness6.734240105
Sum7.558510843 × 1010
Variance4.113511931 × 1014
MonotonicityNot monotonic
2022-02-10T02:32:19.988771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12278282.721491
 
24.2%
8643092
 
< 0.1%
21080681
 
< 0.1%
239424921
 
< 0.1%
36809541
 
< 0.1%
29928311
 
< 0.1%
347782771
 
< 0.1%
49167071
 
< 0.1%
9995001
 
< 0.1%
60948451
 
< 0.1%
Other values (4655)4655
75.6%
ValueCountFrequency (%)
-1855991
< 0.1%
-641011
< 0.1%
-381651
< 0.1%
11
< 0.1%
6401
< 0.1%
8261
< 0.1%
13261
< 0.1%
13641
< 0.1%
16981
< 0.1%
22481
< 0.1%
ValueCountFrequency (%)
4237405761
< 0.1%
3103225111
< 0.1%
3053839511
< 0.1%
2851912311
< 0.1%
2773526661
< 0.1%
2746632651
< 0.1%
2241372441
< 0.1%
2232310711
< 0.1%
2135787271
< 0.1%
1978460421
< 0.1%

unreimbursed_and_uncompensated
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4728
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13968568.12
Minimum-201594
Maximum650380201
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)0.1%
Memory size48.2 KiB
2022-02-10T02:32:20.307730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-201594
5-th percentile410630.75
Q12307436.25
median9441104.5
Q313968568.12
95-th percentile45634426.25
Maximum650380201
Range650581795
Interquartile range (IQR)11661131.87

Descriptive statistics

Standard deviation27594930.94
Coefficient of variation (CV)1.975501763
Kurtosis168.3479459
Mean13968568.12
Median Absolute Deviation (MAD)5950366.5
Skewness10.39342304
Sum8.599050535 × 1010
Variance7.614802138 × 1014
MonotonicityNot monotonic
2022-02-10T02:32:20.637854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13968568.121429
 
23.2%
25559021
 
< 0.1%
22061361
 
< 0.1%
531232941
 
< 0.1%
323440761
 
< 0.1%
19200371
 
< 0.1%
70992041
 
< 0.1%
50675951
 
< 0.1%
947132911
 
< 0.1%
20533371
 
< 0.1%
Other values (4718)4718
76.6%
ValueCountFrequency (%)
-2015941
< 0.1%
-369791
< 0.1%
-229021
< 0.1%
-200241
< 0.1%
-48541
< 0.1%
-33231
< 0.1%
-28371
< 0.1%
-23871
< 0.1%
-9711
< 0.1%
1591
< 0.1%
ValueCountFrequency (%)
6503802011
< 0.1%
6437305871
< 0.1%
5754600141
< 0.1%
4538521961
< 0.1%
3888480531
< 0.1%
3837557501
< 0.1%
3579414901
< 0.1%
3439926811
< 0.1%
3337061971
< 0.1%
3330724201
< 0.1%

Total Costs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6080
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141764161.4
Minimum632028
Maximum4919290757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:20.971457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum632028
5-th percentile6914528.5
Q117963261.25
median45649883
Q3147810674.5
95-th percentile572705641.8
Maximum4919290757
Range4918658729
Interquartile range (IQR)129847413.2

Descriptive statistics

Standard deviation264901400.5
Coefficient of variation (CV)1.868606268
Kurtosis49.41625786
Mean141764161.4
Median Absolute Deviation (MAD)34639626.5
Skewness5.413951667
Sum8.727001773 × 1011
Variance7.017275199 × 1016
MonotonicityNot monotonic
2022-02-10T02:32:21.219383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141764161.477
 
1.3%
207093391
 
< 0.1%
1873500431
 
< 0.1%
484097821
 
< 0.1%
257777771
 
< 0.1%
276125081
 
< 0.1%
773953421
 
< 0.1%
727028471
 
< 0.1%
1150000491
 
< 0.1%
132819701
 
< 0.1%
Other values (6070)6070
98.6%
ValueCountFrequency (%)
6320281
< 0.1%
7575581
< 0.1%
9133141
< 0.1%
10076241
< 0.1%
10453171
< 0.1%
10487201
< 0.1%
10999261
< 0.1%
11186621
< 0.1%
11516091
< 0.1%
12166631
< 0.1%
ValueCountFrequency (%)
49192907571
< 0.1%
40620195411
< 0.1%
32319503671
< 0.1%
29694839591
< 0.1%
29054101171
< 0.1%
27898714911
< 0.1%
27456909671
< 0.1%
27361184921
< 0.1%
27122338641
< 0.1%
25779354131
< 0.1%

outpatient_inpatient_charges
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6079
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639890533.6
Minimum12943
Maximum2.200093212 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:21.531430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12943
5-th percentile8401288
Q139884646.5
median141857623.5
Q3684546975.5
95-th percentile2889180742
Maximum2.200093212 × 1010
Range2.200091918 × 1010
Interquartile range (IQR)644662329

Descriptive statistics

Standard deviation1290221904
Coefficient of variation (CV)2.016316598
Kurtosis53.94126463
Mean639890533.6
Median Absolute Deviation (MAD)125794113.5
Skewness5.627980862
Sum3.939166125 × 1012
Variance1.664672561 × 1018
MonotonicityNot monotonic
2022-02-10T02:32:22.330937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
639890533.678
 
1.3%
19084662541
 
< 0.1%
69346070571
 
< 0.1%
9003818171
 
< 0.1%
7913315361
 
< 0.1%
19349852951
 
< 0.1%
231563731
 
< 0.1%
789940521
 
< 0.1%
1104515231
 
< 0.1%
813255801
 
< 0.1%
Other values (6069)6069
98.6%
ValueCountFrequency (%)
129431
< 0.1%
200191
< 0.1%
250001
< 0.1%
353011
< 0.1%
355461
< 0.1%
380311
< 0.1%
384301
< 0.1%
394961
< 0.1%
432901
< 0.1%
505241
< 0.1%
ValueCountFrequency (%)
2.200093212 × 10101
< 0.1%
1.934691589 × 10101
< 0.1%
1.862045123 × 10101
< 0.1%
1.841550679 × 10101
< 0.1%
1.819002621 × 10101
< 0.1%
1.597517982 × 10101
< 0.1%
1.333779326 × 10101
< 0.1%
1.318971083 × 10101
< 0.1%
1.291380902 × 10101
< 0.1%
1.199756447 × 10101
< 0.1%

total_salaries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct4003
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86947778.94
Minimum2
Maximum2748795093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:22.839214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5676598
Q126517399.75
median86947778.94
Q386947778.94
95-th percentile245171451.5
Maximum2748795093
Range2748795091
Interquartile range (IQR)60430379.19

Descriptive statistics

Standard deviation120786031.9
Coefficient of variation (CV)1.389179038
Kurtosis102.930067
Mean86947778.94
Median Absolute Deviation (MAD)34932962.44
Skewness7.710365374
Sum5.352505271 × 1011
Variance1.458926549 × 1016
MonotonicityNot monotonic
2022-02-10T02:32:23.122211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86947778.942154
35.0%
985661371
 
< 0.1%
246384361
 
< 0.1%
520809441
 
< 0.1%
468379081
 
< 0.1%
486308701
 
< 0.1%
716552431
 
< 0.1%
34516241
 
< 0.1%
374001491
 
< 0.1%
2544578281
 
< 0.1%
Other values (3993)3993
64.9%
ValueCountFrequency (%)
21
< 0.1%
1265611
< 0.1%
4696601
< 0.1%
5267071
< 0.1%
6701311
< 0.1%
6758971
< 0.1%
7058801
< 0.1%
7149971
< 0.1%
7692891
< 0.1%
7825351
< 0.1%
ValueCountFrequency (%)
27487950931
< 0.1%
23753076131
< 0.1%
19068221461
< 0.1%
18612788031
< 0.1%
15774266281
< 0.1%
14834307651
< 0.1%
14465441231
< 0.1%
14223502161
< 0.1%
13713178991
< 0.1%
13607714751
< 0.1%

Contract Labor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct3054
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3988528.353
Minimum118
Maximum272713537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:23.365495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile118361.5
Q11382238
median3988528.353
Q33988528.353
95-th percentile8278310
Maximum272713537
Range272713419
Interquartile range (IQR)2606290.353

Descriptive statistics

Standard deviation7564355.414
Coefficient of variation (CV)1.896527928
Kurtosis365.7203123
Mean3988528.353
Median Absolute Deviation (MAD)0
Skewness15.06330879
Sum2.455338054 × 1010
Variance5.721947283 × 1013
MonotonicityNot monotonic
2022-02-10T02:32:23.618548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3988528.3533100
50.4%
814353
 
< 0.1%
603342
 
< 0.1%
5133351
 
< 0.1%
11211131
 
< 0.1%
133541
 
< 0.1%
12669361
 
< 0.1%
9775291
 
< 0.1%
2320941
 
< 0.1%
4764751
 
< 0.1%
Other values (3044)3044
49.4%
ValueCountFrequency (%)
1181
< 0.1%
1591
< 0.1%
1791
< 0.1%
3001
< 0.1%
5801
< 0.1%
6001
< 0.1%
7481
< 0.1%
7661
< 0.1%
7881
< 0.1%
9171
< 0.1%
ValueCountFrequency (%)
2727135371
< 0.1%
1541237201
< 0.1%
1519757871
< 0.1%
1277175671
< 0.1%
1249928611
< 0.1%
1095379981
< 0.1%
1035005391
< 0.1%
1019152361
< 0.1%
1011586961
< 0.1%
952116611
< 0.1%

cash
Real number (ℝ)

HIGH CORRELATION

Distinct5536
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16975543.01
Minimum-2512769971
Maximum4076539300
Zeros0
Zeros (%)0.0%
Negative749
Negative (%)12.2%
Memory size48.2 KiB
2022-02-10T02:32:23.756314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2512769971
5-th percentile-297579.5
Q138132.75
median1741997.5
Q312481011.25
95-th percentile61981390.5
Maximum4076539300
Range6589309271
Interquartile range (IQR)12442878.5

Descriptive statistics

Standard deviation100872601.1
Coefficient of variation (CV)5.942231184
Kurtosis588.0049988
Mean16975543.01
Median Absolute Deviation (MAD)1791168
Skewness13.6303853
Sum1.045014428 × 1011
Variance1.017528164 × 1016
MonotonicityNot monotonic
2022-02-10T02:32:24.027148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16975543.01494
 
8.0%
58488700011
 
0.2%
50010
 
0.2%
2008
 
0.1%
10008
 
0.1%
20006
 
0.1%
3005
 
0.1%
18004
 
0.1%
223655414
 
0.1%
25004
 
0.1%
Other values (5526)5602
91.0%
ValueCountFrequency (%)
-25127699711
< 0.1%
-11556812851
< 0.1%
-9340233711
< 0.1%
-3960392261
< 0.1%
-3599190001
< 0.1%
-2911719831
< 0.1%
-2685985321
< 0.1%
-2593056921
< 0.1%
-2372530151
< 0.1%
-2078642681
< 0.1%
ValueCountFrequency (%)
40765393001
< 0.1%
22942967191
< 0.1%
18977120741
< 0.1%
18792532691
< 0.1%
11896026071
< 0.1%
10796600001
< 0.1%
9497190331
< 0.1%
9219446531
< 0.1%
8647800071
< 0.1%
8140490001
< 0.1%

account_receivable
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5755
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62822563.37
Minimum-178045756
Maximum3711121305
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)0.5%
Memory size48.2 KiB
2022-02-10T02:32:24.157143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-178045756
5-th percentile1374058.25
Q14842519.5
median17184334.5
Q362822563.37
95-th percentile258486440.2
Maximum3711121305
Range3889167061
Interquartile range (IQR)57980043.87

Descriptive statistics

Standard deviation151605938.1
Coefficient of variation (CV)2.413240243
Kurtosis113.7513275
Mean62822563.37
Median Absolute Deviation (MAD)14856594.5
Skewness8.255001184
Sum3.867357001 × 1011
Variance2.298436046 × 1016
MonotonicityNot monotonic
2022-02-10T02:32:24.274755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62822563.37362
 
5.9%
150405300010
 
0.2%
314396644
 
0.1%
6611184223
 
< 0.1%
161042533
 
< 0.1%
2264002373
 
< 0.1%
7550870373
 
< 0.1%
2607885282
 
< 0.1%
6895892032
 
< 0.1%
2423721412
 
< 0.1%
Other values (5745)5762
93.6%
ValueCountFrequency (%)
-1780457561
< 0.1%
-825405231
< 0.1%
-745890461
< 0.1%
-99159481
< 0.1%
-59727451
< 0.1%
-58774841
< 0.1%
-56657471
< 0.1%
-39593221
< 0.1%
-34796851
< 0.1%
-30549431
< 0.1%
ValueCountFrequency (%)
37111213051
 
< 0.1%
26644214661
 
< 0.1%
25036849981
 
< 0.1%
21114042051
 
< 0.1%
18590934811
 
< 0.1%
17258164181
 
< 0.1%
16851419261
 
< 0.1%
150405300010
0.2%
14862600001
 
< 0.1%
12823573241
 
< 0.1%

assets
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5840
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259262816.9
Minimum-236846363
Maximum1.6733791 × 1010
Zeros0
Zeros (%)0.0%
Negative87
Negative (%)1.4%
Memory size48.2 KiB
2022-02-10T02:32:24.408122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-236846363
5-th percentile2731484.75
Q115841489.75
median57550732.5
Q3238516990.2
95-th percentile959876342.2
Maximum1.6733791 × 1010
Range1.697063736 × 1010
Interquartile range (IQR)222675500.5

Descriptive statistics

Standard deviation911534202.2
Coefficient of variation (CV)3.515869391
Kurtosis206.0416239
Mean259262816.9
Median Absolute Deviation (MAD)51098019
Skewness12.70837102
Sum1.596021901 × 1012
Variance8.308946017 × 1017
MonotonicityNot monotonic
2022-02-10T02:32:24.682790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259262816.9278
 
4.5%
1.6732789 × 101010
 
0.2%
15
 
0.1%
2532847624
 
0.1%
4956569883
 
< 0.1%
1475337143
 
< 0.1%
620441693
 
< 0.1%
5732614753
 
< 0.1%
12783763952
 
< 0.1%
9924070002
 
< 0.1%
Other values (5830)5843
94.9%
ValueCountFrequency (%)
-2368463631
< 0.1%
-2087837441
< 0.1%
-1823490331
< 0.1%
-1243157061
< 0.1%
-1180530621
< 0.1%
-1166679811
< 0.1%
-1117385891
< 0.1%
-769988161
< 0.1%
-729042001
< 0.1%
-718931911
< 0.1%
ValueCountFrequency (%)
1.6733791 × 10101
 
< 0.1%
1.6732789 × 101010
0.2%
1.2320504 × 10101
 
< 0.1%
1.106209504 × 10101
 
< 0.1%
99439959421
 
< 0.1%
90096579621
 
< 0.1%
87442314281
 
< 0.1%
78140390001
 
< 0.1%
78007752051
 
< 0.1%
68940162271
 
< 0.1%

account_payable
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5747
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13835008.17
Minimum-998726734
Maximum2057275120
Zeros0
Zeros (%)0.0%
Negative71
Negative (%)1.2%
Memory size48.2 KiB
2022-02-10T02:32:24.917056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-998726734
5-th percentile129800.75
Q1752021.75
median2791351
Q311237749
95-th percentile44116112.5
Maximum2057275120
Range3056001854
Interquartile range (IQR)10485727.25

Descriptive statistics

Standard deviation84952677.67
Coefficient of variation (CV)6.140413984
Kurtosis317.1121513
Mean13835008.17
Median Absolute Deviation (MAD)2466727
Skewness16.13918324
Sum8.516831032 × 1010
Variance7.216957444 × 1015
MonotonicityNot monotonic
2022-02-10T02:32:25.042717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13835008.17368
 
6.0%
167112400010
 
0.2%
32307164
 
0.1%
76406953
 
< 0.1%
730536673
 
< 0.1%
186904293
 
< 0.1%
387147613
 
< 0.1%
167153872
 
< 0.1%
539753872
 
< 0.1%
12531692
 
< 0.1%
Other values (5737)5756
93.5%
ValueCountFrequency (%)
-9987267341
< 0.1%
-3876167721
< 0.1%
-3512018761
< 0.1%
-2389698061
< 0.1%
-1722842251
< 0.1%
-1648028101
< 0.1%
-1628283061
< 0.1%
-1602581931
< 0.1%
-1462978881
< 0.1%
-1421523751
< 0.1%
ValueCountFrequency (%)
20572751201
 
< 0.1%
167112400010
0.2%
14632303501
 
< 0.1%
11956202981
 
< 0.1%
9020769711
 
< 0.1%
8172695831
 
< 0.1%
7891389711
 
< 0.1%
6935097811
 
< 0.1%
6327778811
 
< 0.1%
6040660001
 
< 0.1%

discharge_payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2987
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582825.034
Minimum40
Maximum85077269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:25.199848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile16446.25
Q1400015
median1582825.034
Q31582825.034
95-th percentile3482011
Maximum85077269
Range85077229
Interquartile range (IQR)1182810.034

Descriptive statistics

Standard deviation3101429.689
Coefficient of variation (CV)1.959426735
Kurtosis235.6678861
Mean1582825.034
Median Absolute Deviation (MAD)0
Skewness12.55576571
Sum9743870910
Variance9.618866118 × 1012
MonotonicityNot monotonic
2022-02-10T02:32:25.331116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1582825.0343164
51.4%
26662
 
< 0.1%
306082
 
< 0.1%
1850342
 
< 0.1%
1787132
 
< 0.1%
24712
 
< 0.1%
1332
 
< 0.1%
65181
 
< 0.1%
77547061
 
< 0.1%
2464301
 
< 0.1%
Other values (2977)2977
48.4%
ValueCountFrequency (%)
401
< 0.1%
881
< 0.1%
1151
< 0.1%
1332
< 0.1%
1651
< 0.1%
2191
< 0.1%
2831
< 0.1%
2951
< 0.1%
3261
< 0.1%
3301
< 0.1%
ValueCountFrequency (%)
850772691
< 0.1%
815145831
< 0.1%
580342511
< 0.1%
508338051
< 0.1%
490476011
< 0.1%
487601681
< 0.1%
452464071
< 0.1%
413376101
< 0.1%
404649711
< 0.1%
369027941
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5894
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean670645341.5
Minimum3
Maximum2.200093212 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2022-02-10T02:32:25.611744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11306111.25
Q146109167.5
median174406566.5
Q3690940487.5
95-th percentile2919452819
Maximum2.200093212 × 1010
Range2.200093212 × 1010
Interquartile range (IQR)644831320

Descriptive statistics

Standard deviation1319793004
Coefficient of variation (CV)1.967944787
Kurtosis55.51887221
Mean670645341.5
Median Absolute Deviation (MAD)154781483.5
Skewness5.743574918
Sum4.128492722 × 1012
Variance1.741853572 × 1018
MonotonicityNot monotonic
2022-02-10T02:32:25.758667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670645341.5255
 
4.1%
3563157324
 
0.1%
58444681012
 
< 0.1%
15724845052
 
< 0.1%
1083241142
 
< 0.1%
9659276212
 
< 0.1%
3276027282
 
< 0.1%
452626441
 
< 0.1%
104296571
 
< 0.1%
7884365011
 
< 0.1%
Other values (5884)5884
95.6%
ValueCountFrequency (%)
31
< 0.1%
796831
< 0.1%
3146011
< 0.1%
3939381
< 0.1%
5107691
< 0.1%
7232331
< 0.1%
8485611
< 0.1%
8495361
< 0.1%
10685281
< 0.1%
11727041
< 0.1%
ValueCountFrequency (%)
2.200093212 × 10101
< 0.1%
2.088095388 × 10101
< 0.1%
1.867724521 × 10101
< 0.1%
1.819002621 × 10101
< 0.1%
1.816561094 × 10101
< 0.1%
1.701736925 × 10101
< 0.1%
1.527025447 × 10101
< 0.1%
1.450204598 × 10101
< 0.1%
1.3337794 × 10101
< 0.1%
1.318971083 × 10101
< 0.1%

net_income
Real number (ℝ)

HIGH CORRELATION

Distinct6054
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7452477.288
Minimum-1098320439
Maximum1623850983
Zeros0
Zeros (%)0.0%
Negative2231
Negative (%)36.2%
Memory size48.2 KiB
2022-02-10T02:32:25.899420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1098320439
5-th percentile-24196725
Q1-1111740.75
median1315531.5
Q39735268.5
95-th percentile69079055.25
Maximum1623850983
Range2722171422
Interquartile range (IQR)10847009.25

Descriptive statistics

Standard deviation75697267.26
Coefficient of variation (CV)10.15732948
Kurtosis127.0619593
Mean7452477.288
Median Absolute Deviation (MAD)4355977.5
Skewness0.5969321842
Sum4.587745018 × 1010
Variance5.730076271 × 1015
MonotonicityNot monotonic
2022-02-10T02:32:26.244660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7452477.28891
 
1.5%
-14
 
0.1%
115363374
 
0.1%
12
 
< 0.1%
4602682
 
< 0.1%
8561472
 
< 0.1%
249711642
 
< 0.1%
46869592
 
< 0.1%
2285644042
 
< 0.1%
-367345161
 
< 0.1%
Other values (6044)6044
98.2%
ValueCountFrequency (%)
-10983204391
< 0.1%
-10730159601
< 0.1%
-10485390031
< 0.1%
-10406452991
< 0.1%
-9199064431
< 0.1%
-8974444881
< 0.1%
-7819906591
< 0.1%
-7672306841
< 0.1%
-7337210151
< 0.1%
-7077876551
< 0.1%
ValueCountFrequency (%)
16238509831
< 0.1%
14509387181
< 0.1%
12578398071
< 0.1%
10146670331
< 0.1%
8344528091
< 0.1%
8225768621
< 0.1%
5547707671
< 0.1%
5340910001
< 0.1%
5267786501
< 0.1%
5200300001
< 0.1%

Interactions

2022-02-10T02:32:14.772446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:48.460260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:50.362098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:52.110600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:53.852187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:55.903947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:57.663831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:59.484787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:01.440643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:03.223799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:05.157481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:06.943308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:08.783540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:10.840992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:12.611495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:14.891711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:48.594742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:50.473686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:52.224078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:53.979399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:56.017817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:57.780267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:59.774569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:01.556220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:03.340780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:05.273397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:07.058186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:08.896951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:10.957669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:12.740485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:15.017491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:48.721570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:50.588994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:52.339781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:54.101014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:56.133189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:57.901384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:59.897757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:01.677122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:03.460915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:05.392783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:07.160091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:09.012377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:11.073763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:12.901854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:15.354188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:48.812566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:50.701612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:52.456430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:54.219130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:56.242560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:31:58.014456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:00.009169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:01.794662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-10T02:32:03.573721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-10T02:32:26.987370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

employee_payrollcharity_carebad_dept_expenceunreimbursed_and_uncompensatedTotal Costsoutpatient_inpatient_chargestotal_salariesContract Laborcashaccount_receivableassetsaccount_payabledischarge_paymentsgross_revenuenet_income
076.724.016000e+03263275.0260140.01045317.02121751.0526707.03.988528e+06226139.05.825594e+062.132822e+062.190083e+061.582825e+062121751.082928.0
1332.714.742004e+061984928.0251208.07795912.065481775.03013251.06.027200e+041018957.03.743684e+073.601246e+073.194630e+062.971800e+0465481775.0-322251.0
296.584.742004e+06500576.0152458.01842485.06049571.01009269.03.988528e+06-9342.08.593747e+063.324948e+061.253219e+061.582825e+066143875.0-1026398.0
3680.495.197140e+054922623.01649925.029173301.0140077180.014212723.03.503400e+05145422.01.418231e+081.110707e+083.467110e+061.145300e+05141432373.0-370783.0
41421.496.721210e+0520492826.014922893.097713796.0297655735.037400149.01.734240e+0551676524.05.864628e+081.069469e+093.477631e+072.896290e+05303053449.0-11676899.0
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Duplicate rows

Most frequently occurring

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